Useful MCP Servers (MCPS)

@daltonnyx

Basic information

About Useful MCP Servers

A collection of standalone Python scripts that implement Model Context Protocol (MCP) servers for various utility functions. Each server provides specialized tools that can be used by AI assistants or other applications that support the MCP protocol.

#mermaid #mcp-server #model-context-protocol-servers#docx-replace

Useful MCP Servers config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "userful-mcps": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "./ytdlp",
        "ytdlp_mcp"
      ]
    }
  }
}

Useful MCP Servers tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Useful Model Context Protocol Servers (MCPS)?

How to use Useful Model Context Protocol Servers (MCPS)?

Clone the repository, install uv, then run each server with uv run --directory <path> (e.g., uv run --directory ./ytdlp ytdlp_mcp). Alternatively, configure an MCP client with the command/args structure shown in the server‑specific JSON configuration examples, passing any required environment variables (like API tokens) via the env property. Dependencies are managed per‑server in pyproject.toml and installed automatically on first run.

Key features of Useful Model Context Protocol Servers (MCPS)

Standalone Python MCP servers for utility functions.
Extract chapters and subtitles from YouTube videos.
Process Word document templates and convert to PDF.
Render PlantUML and Mermaid diagrams to PNG images.
Convert RSS feed content to Markdown with date filtering.
Communicate via standard input/output using JSON messages.

Use cases of Useful Model Context Protocol Servers (MCPS)

A chatbot fetching YouTube video chapters or subtitles for summarization.
Automating Word document generation by replacing placeholders and managing content blocks.
Producing PNG diagrams from PlantUML or Mermaid code inside an AI assistant.
Aggregating RSS feed articles into a Markdown list filtered by date.

Frequently asked questions

What is the Model Context Protocol (MCP)?

MCP is a standardized way for AI assistants to interact with external tools and services. It allows AI models to extend their capabilities by calling specialized functions provided by MCP servers.

How do I install and run a server?

Clone the repository, install `uv` (e.g., `pip install uv`), and run a server with `uv run --directory <path>`. For example: `uv run --directory ./ytdlp ytdlp_mcp`. The first run will automatically install dependencies.

Do I need any API keys or external services?

Some servers require external services: for Mermaid rendering you need a Mermaid Chart API access token set as `MERMAID_CHART_ACCESS_TOKEN`; for PlantUML rendering you need a running PlantUML server (e.g., via Docker). The YouTube, Word, and RSS servers do not require API keys.

What configuration is needed for an MCP client?

Your MCP client must launch the server using `uv run --directory <path>` with the correct script name. The README provides JSON configuration examples for each server, including optional friendly names and the `env` property for setting environment variables.

Can I add my own MCP server to this collection?

Yes. Create a new directory with a `pyproject.toml` (defining the script entry point) and a Python file implementing the MCP logic (using `list_tools`, `call_tool`, and the `serve` function). A development template and instructions are provided in the README.

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